Mathematics and Statistics Ä¢¹½ÊÓƵ
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The Department of Mathematics and Statistics supports an active group of researchers with interests spanning a broad range. There is a strong emphasis on conducting research of regional significance and on the active involvement of both graduate and undergraduate students.
Faculty members and graduate students are currently conducting research in the areas of bioinformatics, biostatistics, computational biology, computational engineering, financial analytics, forensic statistics, numerical analysis, quantitative genetics, Ramsey theory and statistics.
Faculty Ä¢¹½ÊÓƵ
Matt Biesecker
Ä¢¹½ÊÓƵ interests: mathematical modeling, optimization, calculus of variations
Fred Boehm
Ä¢¹½ÊÓƵ interests: biostatistics, with a focus in statistical genetics
Gemechis Djira
Ä¢¹½ÊÓƵ interests: simultaneous inferences, bioassays, longitudinal data analysis, statistical computing, Bayesian analysis, sequential methods
- (with Ramu Sudhagoni)
Xijin Ge
Ä¢¹½ÊÓƵ interests: bioinformatics, genomics, cancer
Felix Gnettner
Ä¢¹½ÊÓƵ interests: depth functions, sequential methods, nonparametric statistics, functional and high-dimensional data analysis, computational statistics
Jung-Han Kimn
Ä¢¹½ÊÓƵ interests: efficient parallel algorithm based on domain decompositions: mathematical analysis, practical implementation
Semhar Michael
Ä¢¹½ÊÓƵ interests: computational statistics with a focus on finite mixture modeling and model-based clustering
- Studying complexity of model-based clustering
Hossein Moradi
Ä¢¹½ÊÓƵ interests: big data, dimension reduction and variable selection, functional data analysis, multivariate statistics, spatial and spatiotemporal statistics
Trang Nguyen
Ä¢¹½ÊÓƵ interests: optimization, optimal control and applications, machine learning, statistical learning
Michael Puthawala
Ä¢¹½ÊÓƵ interests:
- Machine learning: manifold learning, geometric learning, universality
- Math/applied math: inverse problems, scientific computing, optimal transport
Chris Saunders
Ä¢¹½ÊÓƵ interests: forensic inference of source, statistical pattern recognition, approximation theory
Don Vestal
Ä¢¹½ÊÓƵ interests: number theory, combinatorics (especially Ramsey theory)
Sharon Vestal
Student Ä¢¹½ÊÓƵ
Graduate Student Ä¢¹½ÊÓƵ
- Vahid Hosseinzadeh – working with Michael Puthawala
- The intersection of geometric deep learning and power systems stability
- Cole Patten – working with Michael Puthawala and Chris Saunders
- Cole Rausch – working with Michael Puthawala
- Stability of inverses of ReLU activation layers in deep neural networks
Recent Theses and Dissertations
- Matthew Halberg (M.S., 2026):
- Jax Wysong (M.S., 2026):
- Emma Brookman (M.S., 2025):
- Eleanor Cain (M.S., 2025):
- Annamarie Dobbs (M.S., 2025):
- Nathan Meyer (M.S., 2025):
- Edwin Mutimba (M.S., 2025):
- Addy Smith (M.S., 2025):
- Anthony Glackin (M.S., 2024):
- Cole Patten (M.S., 2024):
- Cami Fuglsby (Ph.D., 2023):
- Rachel Bergjord (M.S., 2023):
- Shi Wen Wong (M.S., 2023):
- Skylar Halverson (M.S., 2022):
- Stephanie Liebl (M.S., 2022):
- Rylee Sundermann (M.S., 2022):
- Tessa Sundermann (M.S., 2022):
- Madeline Anne Ausdemore (Ph.D., 2021):
- Nicholas Brown (Ph.D., 2021):
- Jessie Hendricks (Ph.D., 2021):
- Paul May (Ph.D., 2021):
- Rong Zhou (M.S., 2021):
- Abdelbaset Abdalla (Ph.D., 2019):
- Shaopeng Gu (M.S., 2019):
- Amanda Jensen (M.S., 2019):
- Nicholas Stegmeier (M.S., 2019):
